DeMixPert：基于高斯混合模型的分解响应建模用于分布外单细胞扰动预测
DeMixPert: Decomposed Response Modeling with Gaussian Mixtures for OOD Single-Cell Perturbation Prediction
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中文总结 AI 辅助
DeMixPert通过分解扰动响应为系统、扰动特异性及种群变化成分，结合高斯原型可逆网络建模，在未见过扰动的OOD单细胞预测任务中取得优异性能。
中文摘要 AI 辅助
预测未见过的遗传扰动下的全转录组响应仍是一项重大计算挑战，因为准确预测需要同时恢复扰动特异性转录偏移和异质性细胞响应。现有方法常将确定性响应结构与随机种群水平变化纠缠，导致主导的共享模式掩盖了较弱的扰动特异性信号，损害了分布建模。为解决这些挑战，我们提出DeMixPert，一种用于分布外（OOD）单细胞扰动预测的分解响应建模方法。DeMixPert将扰动诱导的变化分解为依赖于基础状态的系统响应、扰动特异性响应和种群水平变化。系统成分源自对照细胞表达编码的基础状态，而扰动特异性成分则从预训练的目标嵌入中推断，用于未见过目标的泛化。DeMixPert使用高斯原型可逆网络对种群水平变化进行建模，并根据基础状态和扰动条件自适应组合可重用的高斯原型，所得混合模型被映射到条件特异性变化分布。采样得到的变化与系统成分和扰动特异性成分整合，随后与基础状态联合解码以重构扰动细胞的基因表达。实验结果表明，DeMixPert能有效捕获异质性单细胞扰动响应，在未见过扰动的设置中实现了优异性能。源代码将在发表后公开提供。
英文摘要
Predicting transcriptome-wide responses to unseen genetic perturbations remains a major computational challenge because accurate prediction requires recovering both perturbation-specific transcriptional shifts and heterogeneous cellular responses. Existing methods often entangle deterministic response structure with stochastic population-level variation, causing dominant shared patterns to mask weaker perturbation-specific signals and impair distributional modeling. To address these challenges, we propose \textbf{DeMixPert}, an approach for Decomposed response Modeling with Gaussian Mixtures for Out-Of-Distribution (OOD) single-cell Perturbation prediction. DeMixPert decomposes perturbation-induced changes into a basal-state-dependent systematic response, a perturbation-specific response, and population-level variation. The systematic component is derived from the basal state encoded from control-cell expression, whereas the perturbation-specific component is inferred from pretrained target embeddings for unseen-target generalization. DeMixPert models population-level variation using a Gaussian prototype Invertible Network and adaptively combines reusable Gaussian prototypes according to the basal state and perturbation condition. The resulting mixture is mapped to a condition-specific variation distribution. Sampled variations are integrated with the systematic and perturbation-specific components, followed by joint decoding with the basal state to reconstruct perturbed-cell gene expression. Experimental results show that DeMixPert effectively captures heterogeneous single-cell perturbation responses and achieves superior performance across unseen-perturbation settings. The source code is made publicly available upon publication.
